There continues to be research and proposed solutions to grammar checking and word usage errors. What these solutions have in common is the targeted design for specific types of errors. For example, in a word processor, grammar checking is one of the processes the word processor was designed to do effectively. Where there is a subject-word agreement problem where the number on the verb does not agree with the number of the subject, the checker can handle this with a high degree of success. These grammar checkers are designed to target a specific set of errors, such as subject-verb disagreement.
Similarly, with respect to learning the English language and grammar, a daunting task for non-English speaking users, typical systems for automatic error correction and detection are designed by identifying the typical errors made, and then developing specialized subsystems that target a specific error. For example, prepositions are difficult for learners because prepositional systems across languages are very different. Thus, a typical system design for automatic correction of learner language specifically looks at preposition errors. To do so, it may consider the assigned part of speech tag for each of the words in a sentence, and then identify noun phrases based on the tags. Further analysis checks whether a preposition precedes the noun phrase and determines which preposition would be the best choice, given the words and parts-of-speech in the context.
These more focused designs employ different models from which to check if the preposition entered by the user was correct, and if no preposition precedes the noun phrase, another model from which to determine if a preposition should be present in this context. The more focused designs are costly in terms of performance and maintenance, since each error type amongst a potentially large set requires its own targeted sub-component.